Abstract
The generation of user-defined optical temporal waveforms with picosecond resolution is an essential task for many applications, ranging from telecommunications to laser engineering. Realizing this functionality in an on-chip reconfigurable platform remains a significant challenge. Towards this goal, autonomous optimization methods are fundamental to counter fabrication imperfections and environmental variations, as well as to enable a wider range of accessible waveform shapes and durations. In this work, we introduce and demonstrate a self-adjusting on-chip optical pulse-shaper based on the concept of temporal coherence synthesis. The scheme enables on-the-fly reconfigurability of output optical waveforms by using an all-optical sampling technique in combination with an evolutionary optimization algorithm. We further show that particle-swarm optimization can outperform more commonly used algorithms in terms of convergence time. Hence, our system combines all key ingredients for realizing fully on-chip smart optical waveform generators for next-generation applications in telecommunications, laser engineering, and nonlinear optics.
Highlights
The incorporation of machine learning and “smart” optimization techniques into photonic technologies has enabled widespread enhancements in device functionalities beyond the capabilities of traditional optical systems [1]
In order to demonstrate the capabilities of our pulse-shaping approach, we tested four waveforms of particular interest for optical signal processing [10,11,12], i.e., positive and negative sawtooth, triangle, and flattop pulses
We demonstrated picosecond pulse shaping by temporal coherence synthesis on a fiber-coupled, reconfigurable split-and-delay line (SDL) chip combined with a cost-effective optical readout and an autonomous optimization technique
Summary
The incorporation of machine learning and “smart” optimization techniques into photonic technologies has enabled widespread enhancements in device functionalities beyond the capabilities of traditional optical systems [1]. The advantage of using machine-learning approaches becomes apparent for the optimization of complex photonic integrated circuits with many degrees of control—where numerical modeling alongside fabrication imperfections makes the task increasingly difficult. An application field that can especially benefit from such approaches is on-chip optical waveform generation. Optical waveform generators (OWGs) are key to boosting optical signal processing functionalities [9]. OWGs enable crucial enhancements in frequency shifting [10], nonlinear conversion processes [11], and all-optical signal processing [12].
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